code: def parse_args(self):
      parser = argparse.ArgumentParser(
          description="""skull_strip pipeline <T1post> <FLAIR> <output_folder> [-gpu_num <int> -niftis -nobias -preprocessed -keep_outputs]
  
              Segment an image from DICOMs with all preprocessing steps included.
  
              -output_folder: A filepath to your output folder. Two nifti files will be generated "enhancingtumor.nii.gz" and "wholetumor.nii.gz"
              -T1POST, -FLAIR: Filepaths to input MR modalities. Inputs can be either nifti files or DICOM folders. Note that DICOM folders should only contain one volume each.
              -mask_output: Name of output for your binary skull mask. Should not be a filepath, like '/home/user/enhancing.nii.gz', but just a name, like "enhancing"
              -gpu_num: Which CUDA GPU ID # to use. Defaults to 0, i.e. the first gpu.
              -debiased: If flagged, data is assumed to already have been N4 bias-corrected, and skips that preprocessing step.
              -resampled: If flagged, data is assumed to already have been isotropically resampled, and skips that preprocessing step.
              -registered: If flagged, data is assumed to already have been registered into the same space, and skips that preprocessing step.
              -save_all_steps: If flagged, intermediate volumes in between preprocessing steps will be saved in output_folder.
              -save_preprocessed: If flagged, the final volume after all preprocessing steps will be saved in output_folder
                  """
      )
  
      parser.add_argument("-output_folder", type=str)
      parser.add_argument("-T1POST", type=str)
      parser.add_argument("-FLAIR", type=str)
      parser.add_argument("-input_directory", type=str)
      parser.add_argument(
          "-segmentation_output",
          nargs="?",
          type=str,
          const="segmentation.nii.gz",
          default="segmentation.nii.gz",
      )
  
      parser.add_argument("-debiased", action="store_true")
      parser.add_argument("-registered", action="store_true")
      parser.add_argument("-preprocessed", action="store_true")
  
      parser.add_argument(
          "-gpu_num", nargs="?", const="0", default="0", type=str
      )
      parser.add_argument(
          "-save_only_segmentations", action="store_true"
      )
      parser.add_argument("-save_all_steps", action="store_true")
      parser.add_argument("-quiet", action="store_true")
      parser.add_argument("-output_probabilities", action="store_true")
      args = parser.parse_args(sys.argv[2:])
  
      return args
  
file_path: DeepNeuro/deepneuro/pipelines/Skull_Stripping/cli.py
imports: import argparse
  import sys
  import os
  from deepneuro.pipelines.shared import DeepNeuroCLI
label_desc: Use image node_id 2 for calling the Skull_Stripping_cli obj's underlying member method code with example usage: obj.parse_args() and returns: args